Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach
For logistics companies needing real-time shipment visibility, this work provides a practical solution to a common data quality problem, though it is an incremental improvement over existing rule-based methods.
The paper tackles the problem of matching trucks to shipments in full truckload logistics when vehicle identifiers are missing or corrupted, using GPS data. The proposed system, ITM 2.0, achieves a 26 percentage point precision improvement in North America and 14 points in Europe, while doubling coverage.
Accurate truck-to-shipment matching using GPS data is foundational for full truckload supply chain visibility, enabling real-time tracking and accurate estimated time of arrival (ETA) predictions. However, missing or corrupted vehicle identifiers prevent traditional matching approaches, leaving shipments without visibility. This paper presents Intelligent Truck Matching (ITM) 2.0, a machine learning system that addresses this critical gap by formulating matching as a probabilistic ranking problem. Our approach leverages Uber H3 hexagonal spatial indexing to discretize GPS pings into route similarity features, combined with temporal information, then applies LightGBM gradient boosting with threshold-based post-processing. Through rigorous evaluation including offline model selection (SVM, XGBoost, LightGBM), comprehensive ablation studies, and production shadow testing, we demonstrate substantial gains over rule-based baselines. ITM 2.0 achieves 26 percentage point precision improvement in North America and 14 points in Europe, while doubling coverage. Deployed in production at Project44 handling full truckload shipments, the system demonstrates robustness to geocoding errors up to 1 km, multiple candidate trucks, and sparse pings.